Homework 5

Author

Sucry Bendeck

Libraries

library(vegabrite)
library(readr)
library(dplyr)

Exercise 1

  • a. Graphic

Cars
  • b. What marks are being used? What variables are mapped to which properties?
    • The marks being used are points. Variables miles per gallon and weight are mapped to the point. Where each point represents a specific car.
  • c. What is the main story of this graphic?
    • This graphic, I think , is telling us a story about cars fuel efficiency in comparison to their weight. Furthermore, the graphic shows that on the lower the weight the better the fuel efficiency.
  • d. What makes it a good graphic?
    • For the most part its an easy graphic to interpret. Labels are clear, how ever there is room for improvement. For example, instead of having static labels for the cars it could perhaps be interactive so that you can hover over the points and then the label appears. Right now all the labels appear which means that many overlap, having the labels this way makes it hard to read the car names.
  • e. What features do you think you would know how to implement in Vega-Lite?
    • I think that I would be able to replicate the graphic.
  • f. Are there any features of the graphic that you would not know how to do in Vega-Lite? If so, list them.
    • I think I would have to investigate the labels. Although I might be able to make it a layer but if i want to implement the hovering interaction I might have to research a little to try and implement that feature.

Exercise 2

  • Create a graphic that shows the mean temperature for each month. How many “months” should you be displaying? (There is more than one answer to this – perhaps try doing it more that one way.)
Code 
Rows: 2922 Columns: 11
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (2): location, weather
dbl  (8): precipitation, temp_max, temp_min, wind, year, month, day, day_of_...
date (1): date

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# A tibble: 6 × 11
  location date       precipitation temp_max temp_min  wind weather  year month
  <chr>    <date>             <dbl>    <dbl>    <dbl> <dbl> <chr>   <dbl> <dbl>
1 Seattle  2012-01-01           0       12.8      5     4.7 drizzle  2012     1
2 Seattle  2012-01-02          10.9     10.6      2.8   4.5 rain     2012     1
3 Seattle  2012-01-03           0.8     11.7      7.2   2.3 rain     2012     1
4 Seattle  2012-01-04          20.3     12.2      5.6   4.7 rain     2012     1
5 Seattle  2012-01-05           1.3      8.9      2.8   6.1 rain     2012     1
6 Seattle  2012-01-06           2.5      4.4      2.2   2.2 rain     2012     1
# ℹ 2 more variables: day <dbl>, day_of_week <dbl>
 num [1:2922] 1 1 1 1 1 1 1 1 1 1 ...
 num [1:2922] 2012 2012 2012 2012 2012 ...
 num [1:2922] 5 2.8 7.2 5.6 2.8 2.2 2.8 2.8 5 0.6 ...
 num [1:2922] 12.8 10.6 11.7 12.2 8.9 4.4 7.2 10 9.4 6.1 ...
# Creating the graphics
vl_chart(data = weather_data, height = 400, width = 600) |>
  vl_mark_bar() |>
  vl_encode_x("month", title = "Month", type = "nominal", sort = "ascending") |>
  vl_encode_y("avg_temp", title = "Average Temperature", type = "quantitative", aggregate = "mean" )|>
  vl_encode_xOffset("year")|>
  vl_encode_color("year",scale=list(scheme = "category10"))
# Creating the graphics
vl_chart(data = weather_data, height = 250, width = 400) |>
  vl_mark_bar() |>
  vl_encode_x("month", title = "Month", type = "nominal", sort = "ascending") |>
  vl_encode_y("avg_temp", title = "Average Temperature", type = "quantitative", aggregate = "mean" )|>
  vl_facet_wrap("year")
Warning in vl_facet_wrap(vl_encode_y(vl_encode_x(vl_mark_bar(vl_chart(data =
weather_data, : vl_facet_wrap has been replaced with vl_facet and will be
removed in future package versions
  • Create a graphic that shows how the different types of weather (rain, fog, etc.) are distributed by month in Seattle. When is it rainiest in Seattle? Sunniest?
# Grouping by weather
weather_data <- weather_data |> group_by(weather)

# Creating the graphics
  vl_chart(data = weather_data, height = 400, width = 600) |>
  vl_mark_bar() |>
  vl_encode_x("month", title = "Month", type = "nominal", sort = "ascending") |>
  vl_encode_y(aggregate = "count", title = "Count", type = "quantitative") |>
  vl_encode_color("weather", type = "nominal", title = "Weather Type", ) |>
  vl_encode_xOffset("weather", type = "nominal", title = "Weather Type")